Bin Chen, Yuefeng Han, Qiyang Yu
arXiv 4 Nov 2025 · Statistics — Methodology
arXiv:2511.02235 · PDF · Extracted main text
In this paper, we consider diffusion index forecasting with both tensor and non-tensor predictors, where the tensor structure is preserved with a Canonical Polyadic (CP) tensor factor model. When the number of non-tensor predictors is small, we study the asymptotic properties of the least squares estimator in this tensor factor-augmented regression, allowing for factors with different strengths. We derive an analytical formula for prediction intervals that accounts for the estimation uncertainty of the latent factors. In addition, we propose a novel thresholding estimator for the high-dimensional covariance matrix that is robust to cross-sectional dependence. When the number of non-tensor predictors exceeds or diverges with the sample size, we introduce a multi-source factor-augmented sparse regression model and establish the consistency of the corresponding penalized estimator. Simulation studies validate our theoretical results and an empirical application to U.S. trade flows demonstrates the advantages of our approach over other popular methods in the literature.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jianqing Fan and Zhipeng Lou and Mengxin Yu (2024) Are Latent Factor Regression and Sparse Regression Adequate? | 1.000 | 5 | 3 | 100% |
| 2 | Rothman, Adam J. and Levina, Elizaveta and Zhu, Ji (2009) Generalized Thresholding of Large Covariance Matrices | 1.000 | 5 | 3 | 100% |
| 3 | Han, Yuefeng and Yang, Dan and Zhang, Cun-Hui and Chen, Rong (2024) CP factor model for dynamic tensors self | 1.000 | 5 | 3 | 100% |
| 4 | Jushan Bai and Serena Ng (2006) Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions | 0.953 | 15 | 7 | 87% |
| 5 | Bai, Jushan (2003) Inferential Theory for Factor Models of Large Dimensions | 0.950 | 7 | 3 | 86% |
| 6 | Bai, Jushan and Ng, Serena (2023) Approximate factor models with weaker loadings | 0.941 | 6 | 3 | 83% |
| 7 | Chen, Bin and Han, Yuefeng and Yu, Qiyang (2026) Estimation and inference for CP tensor factor models self | 0.914 | 17 | 5 | 76% |
| 8 | Fan, Jianqing and Liao, Yuan and Mincheva, Martina (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.811 | 4 | 2 | 100% |
| 9 | James H Stock and Mark W Watson (2002) Macroeconomic Forecasting Using Diffusion Indexes | 0.737 | 4 | 2 | 75% |
| 10 | Peter J. Bickel and Elizaveta Levina (2008) Covariance regularization by thresholding | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 63 scored citations.